Modelling user satisfaction in public transport systems considering missing information
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Modelling user satisfaction in public transport systems considering missing information Eneko Echaniz1 · Chinh Ho2 · Andres Rodriguez1 · Luigi dell’Olio1
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Collecting data to obtain insights into customer satisfaction with public transport services is very time-consuming and costly. Many factors such as service frequency, reliability and comfort during the trip have been found important drivers of customer satisfaction. Consequently, customer satisfaction surveys are quite lengthy, resulting in many interviews not being completed within the aboard time of the passengers/respondents. This paper questions as to whether it is possible to reduce the amount of information collected without a compromise on insights. To address this research question, we conduct a comparative analysis of different Ordered Probit models: one with a full list of attributes versus one with partial set of attributes. For the latter, missing information was imputed using three different methods that are based on modes, single imputations using predictive models and multiple imputation. Estimation results show that the partial model using the multiple imputation method behaves in a similar way to the model that is based on the full survey. This finding opens an opportunity to reduce interview time which is critical for most customer satisfaction surveys. Keywords Missing information · Multiple imputation · User satisfaction · Ordered probit · Perceived quality
Introduction Research on the perceived quality or the satisfaction of the users usually relies on customer satisfaction surveys conducted using a revealed preference survey method. Data collection is usually the most time-consuming and costly part, especially when a face-to-face survey method is used. While this survey method undoubtedly delivers the high data quality, its completion/response rate depends heavily on the interview duration with lengthy questionnaire resulting in a lower response/completion rate. Thus, finding a way to shorten the survey length would improve the effectiveness of customer satisfaction studies. This article * Eneko Echaniz [email protected] 1
Department of Transportation, University of Cantabria, Santander, Spain
2
Institute of Transport and Logistics Studies, The University of Sydney Business School, Sydney, Australia
13
Vol.:(0123456789)
Transportation
proposes a way to do so through a comparative analysis of different models. These models are based on data from customer satisfaction surveys with full and partial list of attributes. Partial dataset is obtained after randomly deleting half of the information available in the original survey. No statistical difference between the two methods will mean that it is possible to reduce the amount of data collected in customer satisfaction surveys. To this end, missing data are imputed using three different methods in order to identify the most adequate method for imputing non-collected information. The first method uses t
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